Forecasting Risk

Neil Smith Neil considers the potential of long-range forecasting for the insurance industry.

We are at the beginning of another hurricane season and everyone is waiting with bated breath to see if it will be “third time lucky” for the insurance industry with no major hurricanes making US landfall for the third year in a row.

As always we have seen the usual round of seasonal forecasts and all are predicting another active season. It therefore seems a good time to consider how the insurance industry uses forecasts to inform decision-making and whether it is time to investigate new methods and techniques given the recent advances in technology and climate research.

Forecasting scientists are now developing models to predict weather events and patterns over timeframes from the seasonal to several years. Increasingly sophisticated forecasting models take into account ocean and atmosphere conditions, as well as seasonal and regional climate trends, such as El Nino. Forecasters are also refining the skill of these models which should make them more relevant to the premium setting and capital decisions insurers make.

As the impacts of climate change are increasingly felt, and in particular the growing evidence that climate change is resulting in more frequent severe weather events including hurricanes, such longer-range forecasting approaches should play an increasingly important role in the insurance industry.

Lloyd’s recently held a series of expert workshops to explore the potential of long-range forecasting for the insurance industry and used the outputs from these sessions to prepare a Forcasting Report. This report, jointly produced with the Met Office, highlights both the challenges and potential benefits of introducing long-term forecasting into the insurance industry.

Comments

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Tue 05 Jul
Andrew

An interesting report. Models have developed to a point where some at least should be taken seriously by governments. For insurers it would be of value to know the problem the other way round though. Insurers already know what the effect of exposure and exposure uncertainty is on price, reinsurance and reserving. They know what the effect on their business would be if their assumptions were proved wrong. They know how wrong they can be and survive. They know how wrong they can be and make a good profit. The commercially useful precision and accuracy of dynamic models over a commercially relevant time-scale can therefore be specified in advance. This will vary from insurer to insurer. The insurer can then ask that his needs from modelling be provided for. Rather than making do with what the modellers can deliver, tell them what the required standard is. Governments and insurers both have a tolerance for model error, but are they the same?







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